Robust Hammerstein Adaptive Filtering under Maximum Correntropy CriterionReport as inadecuate




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1

School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510640, China

2

School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China

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School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China





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Academic Editor: Kevin H. Knuth

Abstract The maximum correntropy criterion MCC has recently been successfully applied to adaptive filtering. Adaptive algorithms under MCC show strong robustness against large outliers. In this work, we apply the MCC criterion to develop a robust Hammerstein adaptive filter. Compared with the traditional Hammerstein adaptive filters, which are usually derived based on the well-known mean square error MSE criterion, the proposed algorithm can achieve better convergence performance especially in the presence of impulsive non-Gaussian e.g., α-stable noises. Additionally, some theoretical results concerning the convergence behavior are also obtained. Simulation examples are presented to confirm the superior performance of the new algorithm. View Full-Text

Keywords: Hammerstein adaptive filtering; MCC; nonlinear system identification Hammerstein adaptive filtering; MCC; nonlinear system identification





Author: Zongze Wu 1, Siyuan Peng 1, Badong Chen 2,* and Haiquan Zhao 3

Source: http://mdpi.com/



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